Reduced order models (ROMs) often refer to simplifications of high-fidelity models that capture dominant system dynamics using minimal computational resources. Over the past decades, we have witnessed an ever increasing number of reduced order modeling approaches and their enormous impact on fluid dynamics research [1]}, [2]}, [3]}, [4]}. A chief motivation behind these approaches being introduced is to be able to use ROMs in multi-query applications such as control and optimization [5]}, [6]}.
